Standard federated learning solves the problem of \(N\)  workers trying to learn a shared global model with the aid of a parameter server (PS). Each worker is exposed to limited data samples and is not willing to share its data due to privacy and/or communication constraints [1]}, [2]}, [3]}. The PS starts with an initial guess of the global model, shares it with all contributing workers where each worker performs a local computation step before sharing its output. The local computation step could be (i) calculating the gradient of the local function with respect to the global model using a randomly selected mini-batch [4]} or (ii) performing a number of local stochastic gradient descent (SGD) steps using the global model as initializer  [5]}. Then the PS aggregates the outputs of all workers and performs one global step which is one SGD step using the aggregated gradients of all workers for (i), or model averaging for (ii). Under some assumptions, this leads to convergence to a global model shared across workers.
